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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Google Cloud Platform Engineer AI Agent Development (ADK) - **Company:** HMG America - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, BigQuery, Cloud Computing, Cloud Engineering, Continuous Integration, Information Engineering, Data Governance, Software Debugging, DevOps, Github, Identity and Access Management, Python (Programming Language), Performance Tuning, Systems Integration, Enterprise Data Management, Data Logging, Google Cloud, Cloud Platform System, Chatbots, Cloud Monitoring, Large Language Models, Multi-Agent Systems, Build Server, Amazon Virtual Private Cloud (VPC), Event Driven Architecture, Low Latency, Deployment Automation, Google Cloud Functions, Api Design, Terraform, Microservices - **Published:** August 28, 2026 - **Apply:** https://www.dice.com/job-detail/ba3d74d7-d269-4742-bafa-5f80db119c3c ## About the Role * 4+ years of experience as a Cloud, Software, or ML Engineer with significant hands-on experience on the Google Cloud Platform. * Practical experience building AI agents using Google Agent Development Kit (ADK) or comparable agent frameworks such as: + LangChain / LangGraph + CrewAI + AutoGen + Semantic Kernel * Strong proficiency in Python; familiarity with Java or Go is a plus. * Hands-on experience with Google Cloud Platform services, particularly Vertex AI and cloud-native infrastructure. * Experience building AI agents, RAG pipelines, tool/function calling, and agent orchestration. * Experience with Infrastructure-as-Code, such as Terraform or Google Cloud Deployment Manager. * Experience with CI/CD tooling and deployment automation. * Understanding of API design, microservices architecture, and event-driven systems. * Strong debugging, performance optimization, and problem-solving skills. * Experience developing secure and scalable production applications in the cloud. Nice to Have * Experience with multi-agent orchestration patterns, including agent-to-agent protocols and hierarchical/supervisor agents. * Familiarity with Model Context Protocol (MCP) or similar tool-integration standards. * Google Cloud certification, such as: + Professional Cloud Architect + Professional ML Engineer + Associate Cloud Engineer * Experience with observability and evaluation tooling for LLM applications, including tracing, hallucination detection, and evaluation metrics. * Background in enterprise workflow automation, chatbots, or conversational AI. * Exposure to other cloud platforms such as AWS or Azure. * Experience working in telecom or large B2C enterprise environments. Preferred Technical Skills Cloud: Google Cloud Platform, Vertex AI, GKE, Cloud Run, Compute Engine, Cloud Functions, Pub/Sub, BigQuery AI/ML: Gemini, Agent Development Kit (ADK), Agent Engine, RAG, LLMs, AI Agents, Function Calling Agent Frameworks: ADK, LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel Programming: Python, Java/Go DevOps: Terraform, Cloud Build, GitHub Actions, CI/CD Architecture: Microservices, APIs, Event-Driven Architecture, Multi-Agent Systems Observability: Cloud Monitoring, Cloud Trace, Logging, LLM Evaluation ## Description We are looking for a Google Cloud Platform (Google Cloud Platform) Engineer with hands-on experience building intelligent AI agents using Google's Agent Development Kit (ADK). In this role, you will design, build, deploy, and optimize production-grade AI agents and cloud-native systems that automate workflows, integrate with enterprise data, and scale reliably on Google Cloud Platform. You will work at the intersection of cloud infrastructure and applied AI, turning agentic AI prototypes into robust, observable, secure, and production-ready services., * Design, develop, and deploy autonomous and multi-agent systems using Google Agent Development Kit (ADK), integrating tools, memory, and orchestration logic. * Architect and manage scalable, secure infrastructure on Google Cloud Platform, including Compute Engine, GKE, Cloud Run, Cloud Functions, Pub/Sub, BigQuery, and Vertex AI. * Build agent-to-agent (A2A) and agent-to-tool integrations, including function calling, RAG pipelines, APIs, and tool orchestration. * Deploy and manage agents using Vertex AI Agent Builder / Agent Engine and integrate with Gemini models via Vertex AI. * Implement CI/CD pipelines for agent and infrastructure deployment using Cloud Build, Terraform, GitHub Actions, or similar tools. * Set up monitoring, logging, tracing, and evaluation frameworks for agent behavior and performance using Cloud Monitoring, Cloud Trace, and custom evaluation harnesses. * Collaborate with AI, product, and data engineering teams to translate business workflows into agentic AI solutions. * Ensure AI agents meet security, privacy, and compliance requirements, including IAM, VPC Service Controls, and data governance. * Optimize the cost, latency, scalability, and reliability of agent workloads running on Google Cloud Platform. * Write clean, well-tested, production-ready Python code and maintain technical documentation. * Apply strong debugging, performance-tuning, and problem-solving skills to cloud and AI workloads. * Work with API design, microservices architecture, and event-driven systems. ## Related Videos - [Designing and Deploying Distributed Multimodal Multi-Agent Systems with Google's AI Stac](https://www.wearedevelopers.com/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)